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AutoML systems can speed up routine data science work and make machinelearning available to those without expertise in statistics and computerscience. These systems have gained traction in enterprise settings where poolsof skilled data workers are limited. In this study, we conduct interviews with29 individuals from organizations of different sizes to characterize how theycurrently use, or intend to use, AutoML systems in their data science work. Ourinvestigation also captures how data visualization is used in conjunction withAutoML systems. Our findings identify three usage scenarios for AutoML thatresulted in a framework summarizing the level of automation desired by dataworkers with different levels of expertise. We surfaced the tension betweenspeed and human oversight and found that data visualization can do a poor jobbalancing the two. Our findings have implications for the design andimplementation of human-in-the-loop visual analytics approaches.

Fits and Starts: Enterprise Use of AutoML and the Role of Humans in the Loop